Abstract: Example computer-implemented methods, media, and systems for identification and characterization of geologic features in carbonate reservoir are disclosed. One example computer-implemented method includes obtaining multiple core sample images of a carbonate reservoir. The multiple core sample images are labeled using multiple feature classes, where the multiple feature classes include at least one of a vug or fracture. Multiple image patches are generated using the labeled plurality of core sample images. A machine learning model is applied to the multiple image patches to identify one or more vugs or fractures in the multiple core sample images. At least one of porosity or permeability of the carbonate reservoir is predicted using the identified one or more vugs or fractures in the multiple core sample images.

Abstract: A method may use a core sampling system for collecting a core sample with a reference log of a first property. The method may use a wellbore logging system for recording uncalibrated well logs with a target log of the first property. The method may use a computer processor for obtaining an uncalibrated geological model, determining a bulk-shift depth correction based on a first cost function, forming a bulk-shifted log by applying the bulk-shift depth correction to the target log, identifying a plurality of log event pairs, determining, for each of the log event pairs, a local-shift depth correction based on a second cost function, forming a local-shift depth correction table from the local-shift depth correction for the log event pairs, and forming a calibrated geological model based, at least in part, on the uncalibrated geological model and the local-shift depth correction table.

Abstract: Systems and methods for automatic well integrity log interpretation verification are disclosed. The methods include obtaining a first dataset comprising casing thickness profiles and associated electromagnetic [EM]data from at least a first hydrocarbon well having a casing; selecting a training dataset using at least a subset of the casing thickness profiles and a subset of the associated EM data; and training, using the training dataset, a machine learning network to produce a predicted corrosion log of a target section of a second hydrocarbon well from measured EM data from the second hydrocarbon well.

Abstract: Systems and methods for automatic well log normalization are disclosed. The methods include acquiring a target log in a target well and a reference log in a reference well; identifying a stratigraphic interval in the target well and in the reference well; projecting the target log from the target well onto a pseudo target log in a pseudo target well and projecting the reference log from the reference well onto a pseudo reference log in a pseudo reference well; identifying lithologies from a first histogram of the pseudo target log and a second histogram of the pseudo reference log; determining, using the first histogram and the second histogram a regression relationship between the pseudo target log and the pseudo reference log; applying the regression relationship to the pseudo target log to generate a normalized pseudo target log; and determining a reservoir quality of the reservoir that produces hydrocarbons.

Abstract: A method may include obtaining core image data regarding a geological region of interest. The method may further include obtaining well log data regarding the geological region of interest from one or more wells. The method may further include determining a sliding window that corresponds to a predetermined window size. The method may further include determining various quantitative image attributes using the core image data, the well log data, and the sliding window. The quantitative image attributes may be determined in a continuous manner by moving the sliding window along the core image data. The method may further include generating predicted rock data for the geological region of interest using the quantitative image attributes, a machine-learning algorithm, and a machine-learning model.

Abstract: A computer-implemented method includes: accessing a first database holding information encoding a set of labels that specify a condition of at least one of: a surface pipe, or an underground enclosure that runs at a plurality of depth locations; accessing a second database holding a plurality of inspection logs that record measurement data of the surface pipe or underground enclosure; based on, at least in part, the labeling information and the plurality of inspection logs, training a deep learning model configured to classify, into the set of labels, the condition of the surface pipe or underground enclosure when presented with the inspection logs; applying the deep learning model to one or more newly received inspection logs containing measurement data of a new surface pipe or a new underground enclosure; and subsequently classifying, into the set of labels, the condition of the new surface pipe or the new underground enclosure.

Abstract: A method for constructing a high fidelity logging while drilling (LWD) log that includes obtaining a temporal well depth log and a plurality of temporal LWD logs from a drilling operation. The method further includes obtaining a temporal record of operational drilling parameters from the drilling operation. The method further includes determining, using a first machine-learning model, a temporal history of the drilling operation and constructing, using the temporal well depth log and the plurality of temporal LWD logs, a plurality of temporal property logs at each depth in the plurality of depths. The method further includes processing the plurality of temporal property logs with, at least, a second machine-learning model to form a plurality of corrected temporal property logs and aggregating the plurality of corrected temporal property logs to form a plurality of high fidelity LWD logs.

Abstract: A method for analyzing rock cores of a subterranean formation is disclosed. The method includes capturing core images of the rock cores that are collected from geographical locations in the subterranean formation, generating, by a computer processor and from the core images, sub-images by sub-dividing each of the core images, classifying, using a secondary machine learning model that automatically identifies artifacts induced from preparation of the rock cores, the sub-images into artifact-free sub-images and artifact-containing sub-images, and analyzing, using a primary machine learning model, the artifact-free sub-images to generate a core analysis result.

Abstract: Implementations provide a computer-implemented method that includes: accessing a first database holding results of interpreting casing integrity, wherein each result provides a first or a second label for a detected anomaly at a depth location of an inspection log that records electromagnetic (EM) survey data of an underground metal casing; accessing a second database holding inspection logs, each recording EM survey data of a corresponding underground metal casing; training a deep learning model configured to classify an input inspection log into the first or the second label; applying the deep learning model to one or more unclassified inspection logs of the second database, wherein the one or more unclassified inspection logs of the second database comprising anomalies; and subsequently classifying the one or more unclassified inspection logs of the second database into either the first label or the second label.

Abstract: A method to perform a field operation with well log repeatability verification is disclosed. The method includes generating, by repeatedly performing well logging of a wellbore penetrating a subterranean formation in a field, a set of well log data files each comprising a plurality of data channels, each data channel comprising a series of measurement data records representing a downhole property along a depth in the wellbore, analyzing, by a computer processor, a main log and a repeat log of the set of well log data files to determine a repeatability measure of the set of well log data files, presenting, using a graphical user interface, the repeatability measure to a user, and facilitating, based on a user input in response to presenting the repeatability measure, the field operation.

Abstract: A method and a system for well log data quality control is disclosed. The method includes obtaining a well log data regarding a geological region of interest, verifying an integrity and a quality of the well log data, determining the quality of the well log data based on a quality score of the well log data and making a determination regarding the access to the databases based on the quality of data. Additionally, the method includes performing the statistical analysis and the classification of well log data, a predictive and a prescriptive analysis of trends and predictions of the well log data, and generating an action plan for datasets with unsatisfactory quality scores.

Abstract: A method to perform a field operation with rapid well log validation is disclosed. The method includes generating, by performing well logging of a wellbore penetrating a subterranean formation in a field, a well log data file including a number of data channels, each data channel including a series of measurement data records representing a downhole property along a depth in the wellbore, analyzing, by a computer processor, the data channels to determine a quality level of said each data channel, categorizing, by the computer processor, the data channels according to respective values of the quality level to generate a validation summary of the well log data file, presenting, using a graphical user interface, the validation summary to a user, and facilitating, based on a user input in response to presenting the validation summary, the field operation.

Abstract: A method to perform a field operation with well log repeatability verification is disclosed. The method includes generating, by repeatedly performing well logging of a wellbore penetrating a subterranean formation in a field, a set of well log data files each comprising a plurality of data channels, each data channel comprising a series of measurement data records representing a downhole property along a depth in the wellbore, analyzing, by a computer processor, a main log and a repeat log of the set of well log data files to determine a repeatability measure of the set of well log data files, presenting, using a graphical user interface, the repeatability measure to a user, and facilitating, based on a user input in response to presenting the repeatability measure, the field operation.

Abstract: A computer-implemented method, medium, and system for geological core property prediction using machine learning modeling are disclosed. In one computer-implemented method, multiple imagery data of a core sample of a wellbore are received. The multiple imagery data are partitioned into multiple image patches. Multiple first vectors of encoded features in a latent space are generated based on the multiple image patches. Multiple image features of the core sample of the wellbore are generated based on the multiple imagery data. Multiple second vectors of encoded features in the latent space are generated based on the multiple image features. Multiple rock properties associated with the core sample of the wellbore are predicted by running a regressor in the DFCN based on the multiple first vectors and the multiple second vectors. The multiple rock properties are provided for determining multiple properties of a subsurface reservoir that includes the wellbore.

Abstract: Systems, methods, and apparatus including computer-readable mediums for managing training wells for target wells in machine learning are provided. In one aspect, a method includes: for each training well of a plurality of training wells, building a training network for the training well based on well log data of the training well, predicting a target well log of a target well using the training network built for the training well, determining a relevancy level between the training well and the target well based on the predicted target well log of the target well and a measured target well log of the target well, and selecting relevant training wells among the plurality of training wells based on the relevancy levels associated with the plurality of training wells.

Abstract: A computer-implemented method includes: accessing a first database holding information encoding a set of labels that specify a condition of at least one of: a surface pipe, or an underground enclosure that runs at a plurality of depth locations; accessing a second database holding a plurality of inspection logs that record measurement data of the surface pipe or underground enclosure; based on, at least in part, the labeling information and the plurality of inspection logs, training a deep learning model configured to classify, into the set of labels, the condition of the surface pipe or underground enclosure when presented with the inspection logs; applying the deep learning model to one or more newly received inspection logs containing measurement data of a new surface pipe or a new underground enclosure; and subsequently classifying, into the set of labels, the condition of the new surface pipe or the new underground enclosure.

Abstract: Implementations provide a computer-implemented method that includes: accessing a first pool of input data encoding a plurality of petrophysical properties of a first set wells of a reservoir; performing one or more petro-rock type (PRT) labeling at least in part based on the first pool of input data; at least in part based on the one or more petro-rock type (PRT) labeling, training one or more models for the reservoir using one or more machine learning algorithms; accessing a second pool of input data encoding the plurality of petrophysical properties of a second set of wells of the reservoir, and applying the one or more models to a second pool of input data to determine a characteristic of the reservoir, wherein the second set of wells are different from the first set of wells.

Abstract: Systems and methods of optimizing a new well path using a minimum curvature method are disclosed. An arc of the new well path may include a change in curvature at a point along the length of the arc. The arc of the new well path may be determined by iteratively: selecting a length of a first arc portion of the arc; determining a length of a second arc portion of the arc according to a minimum curvature method; combining the first arc portion and the second arc portion to form an arc; determining a deviation of the arc relative to a planned well trajectory; and selecting the arc with the lowest deviation from the planned well trajectory.

Abstract: A method may include obtaining core image data regarding a geological region of interest. The method may further include obtaining well log data regarding the geological region of interest from one or more wells. The method may further include determining a sliding window that corresponds to a predetermined window size. The method may further include determining various quantitative image attributes using the core image data, the well log data, and the sliding window. The quantitative image attributes may be determined in a continuous manner by moving the sliding window along the core image data. The method may further include generating predicted rock data for the geological region of interest using the quantitative image attributes, a machine-learning algorithm, and a machine-learning model.

Abstract: Systems and methods of optimizing a new well path using a minimum curvature method are disclosed. An arc of the new well path may include a change in curvature at a point along the length of the arc. The arc of the new well path may be determined by iteratively: selecting a length of a first arc portion of the arc; determining a length of a second arc portion of the arc according to a minimum curvature method; combining the first arc portion and the second arc portion to form an arc; determining a deviation of the arc relative to a planned well trajectory; and selecting the arc with the lowest deviation from the planned well trajectory.